Electrical Microgrids Research Articles

Optimal design and three-level stochastic energy management for an interconnected microgrid with hydrogen production and storage for fuel cell electric vehicle refueling stations

A new trend in the transportation sector globally can be observed in a shift away from gasoline-powered vehicles to Hydrogen-based fuel-cell electric vehicles (FCEVs), aimed at reducing harmful emissions. However, there are challenges in producing hydrogen for vehicle stations related to microgrid design and energy management under uncertain conditions. This research sought to identify the optimum design of an electric microgrid to provide the required energy for electric loads, together with a hydrogen refueling station. The microgrid under study consists of various renewable energy resources (RERs), such as photovoltaic (PV) devices, wind power systems, and hydrogen storage systems. The energy management strategy (EMS) aims to reduce the total costs (investment, operation, replacement, procurement energy costs) considering four uncertain parameters associated with PV panels, FCEVs, wind turbines, and power demand. A three-level EMS is proposed based on testing various solutions: without RERs or a hydrogen energy storage system (Level 1); with RERs and a hydrogen energy storage system (Level 2), with RERs and hydrogen energy storage that includes demand side response (DSR) (Level 3). The results indicate annual cost savings of 1.946 E+06 $ for Level 2 and 2.001 E+06 $ for Level 3, compared to Level 1.

Dynamic model of integrated electricity and district heating for remote communities

District heating networks offer promising solutions for remote communities, providing centralized heat supply, improved efficiency, and diverse energy sources, especially with existing diesel generation. Hence, this paper bridges gaps in the existing literature by developing comprehensive dynamic models of combined district heating networks within existing electric power networks in remote communities, which allows identifying challenges and benefits of district heating networks for these communities. It is shown that district heating networks allow utilizing waste energy to enable energy exchanges between the electricity and heating systems, enabling the provision of necessary ancillary services for remote microgrids with renewable energy sources. The presented dynamic district heating network model incorporates particular considerations in remote, northern communities such as soil limitations, extreme cold conditions, and piping insulation to minimize heat loss. It also addresses accurate sizing of heat pumps based on realistic thermal load requirements, weather conditions, and consumer profiles, proposing demand management controls to enhance frequency regulation for the integration of variable renewable energy sources. The main contributions of the paper include detailed dynamic modeling for district heating network operation, heat pump demand response control system design, and a comparative analysis between centralized district heating networks and decentralized electric thermal storage units that have been deployed for thermal supply in remote areas. The presented dynamic models are applied, tested, and validated in an existing electric microgrid at Kasabonika Lake First Nation in Northern Ontario, showcasing the role of a potential district heating network in facilitating renewable energy sources integration in isolated microgrids.

Optimum System for Diagnosing Power Quality in Electrical Microgrids

Optimum System for Diagnosing Power Quality in Electrical Microgrids

DYNAMIC MODELLING OF THREE-PHASE MICROGRIDS INCLUDING DQ0 CURRENT CONTROLLED PHOTOVOLTAIC GENERATION UNDER TRANSIENT CONDITIONS

ABSTRACT: The photovoltaic generation can be interconnected to power systems or to insulated grids to satisfy the growing electrical demand as an alternative renewable energy source. The photovoltaic generator dynamics affects the control, protection, stability, and power quality of the electrical grid, this network dynamics requires to be examined and conveniently regulated and adjusted. The dynamic averaged model for the dq0 current controlled DC/AC inverter is applied to obtain the time-domain response to electromagnetic transients under special conditions such as electrical load fluctuations, unbalanced faults, voltage and current transients to verify e.g., the ride through capability or to adjust the protection and control devices, these transients can be present during the operation of electrical microgrids and power systems interconnected with photovoltaic generation. The system modelling includes the photovoltaic plant interconnected with the microgrid using the dq0 current controlled DC/AC inverter. The case studies are applied to analyse three-phase electrical microgrids, their time-domain response, and the obtained voltage and current waveforms can be used to verify the dynamic behaviour and stability of the electrical system interconnected with photovoltaic generation and similarly to verify the operation of the dq0 current controller for the inverter. The results can be used to control the microgrid dynamic behaviour. Keywords: current control, dq0 transform, inverter, microgrids, photovoltaic generation, power quality, protection, time-domain, transients, unbalanced faults, unbalanced voltage sags, voltage source converter

Multi-mode monitoring and energy management for photovoltaic-storage systems

The integration of photovoltaic generation systems and variable demand can cause instability in the distribution network, due to power fluctuations and the increase in reactants, particularly in the industrial sector. In response, photovoltaic units have been equipped with local storage systems, which eventually absorb power fluctuations and improve installation performance. However, during this procedure other functionalities that energy storage could provide are neglected. Consequently, this study provides a multi-mode energy monitoring and management model that enables voltage regulation, frequency regulation and reactive power compensation through the optimal operation of energy storage systems. With this objective, a smoothing control algorithm is developed that interacts with parameters of the electrical grid at the common connection point and also allows the compensation of reactive power based on an industrial demand profile. This strategy uses the Long short-term memory neural network of historical demand data prior to energy consumption with a relatively low RMSE of 1.2e-09. The results are previously validated in a development environment using a real-time OPAL-RT simulator and tests in the electrical Microgrid laboratory at the University of Cuenca. This configuration allows establishing a demand forecasting model that improves the supervision, automation and analysis of daily energy production. A series of results are provided and analyzed that demonstrate that the new tool allows taking advantage of the provision of multimode functionalities, achieving optimal voltage regulation and improving power quality by reducing the total harmonic distortion THD (V) and THD (I) indices by 0.5. % and 2 % respectively.

Economic Energy Scheduling of electrical micro grid considering optimal participation of the electric vehicles

This research presents a strategy for managing energy scheduling within an electrical microgrid, with a specific focus on enhancing the integration of electric vehicles (EVs). By incorporating Monte Carlo simulation to address uncertainties related to EV charging power and demand-side variables, the study aims to ensure precise outcomes. The economic energy scheduling is conducted on a day-ahead basis, taking these uncertainties into consideration to assess the efficiency of the recommended approach. The primary objective is to reduce the overall system costs, encompassing operational expenditures and EV charging power. To tackle the intricacies of the operational framework, the study utilizes the modified sunflower optimization (MSFO) algorithm to resolve the outlined issue. The simulation findings highlight the superior performance of the proposed optimization algorithms compared to others. The proposed approach leads to minimize cost of microgrid by 4.31%, 3.82% and 1.87% than genetic algorithm (GA), Particle swarm optimization (PSO) algorithm and Teaching learning-based optimization (TLBO) algorithm, respectively.

Enhancing electromagnetic compatibility and energy efficiency of electric vehicle charging stations

Problem. The article proposes a single-link structure for an electric vehicle charging station utilizing an active four-square rectifier with power factor correction. A Matlab model of the proposed charging station is developed, taking into account parameters such as the power network, the switches of the active rectifier, its automatic control system, and an equivalent model of the battery compartment. Additionally, a mathematical model for calculating static and dynamic losses is created based on polynomial approximation of the energy dependencies of IGBT modules. The analysis investigates power quality parameters, components of energy losses, and efficiency of the charging station across various charge currents and PWM frequencies during a full battery charge interval. Goal. The aim of this study is to propose a single-link structure for an electric vehicle charging station using an active four-square rectifier with power factor correction. It includes an analysis of power quality parameters, components of energy losses, and efficiency of the charging station at different charge currents and PWM frequencies during a full battery charge interval. Methodology. To achieve the goal, several key steps are considered. These include theoretical substantiation of the scheme of the electric microgrid charging station for electric vehicles with one-stage energy conversion, analysis of the battery connection scheme in the Tesla Model S electric car, research and calculation of efficiency, modeling of the charging station, development of a Matlab model of a microgrid system for the charging station, SAC analysis of battery charge voltage and current of a three-phase AV with PWM, modeling of losses in IGBT modules by polynomial approximation of dependencies, distribution of losses in the charging station system, and analysis of energy efficiency parameters. Results. The study presents the energy efficiency parameters of an external DC EV charging station using an active rectifier. It reveals that maximum efficiency of the system is achieved at minimum charge current. However, decreasing the charge current prolongs the charge process and slightly affects power quality parameters. Originality. A mathematical model for calculating static and dynamic losses was developed based on polynomial approximation of the energy dependencies of IGBT modules. The analysis encompasses power quality parameters, components of energy losses, and efficiency of the charging station across various charge currents and PWM frequencies during a full battery charge interval. Practical value. This study contributes to the further development of electric vehicles by improving the energy indicators of electric vehicle batteries and converters of electric vehicle charging stations, enabling fast charging modes. Active development is observed in each of these directions.

Secondary Control Strategy without Communications for Unbalanced Isolated Microgrids

In the context of the energy transition, electrical microgrids have become a key energy solution for isolated zones. For this type of application, low-voltage networks with unbalanced loads are commonly connected. Despite this, many of the hierarchical control strategies presented in the literature have been designed to operate properly only in the presence of balanced loads. For this reason, it is relevant to study how control strategies can be adapted to this scenario, especially those that reduce the dependence on communications to enhance flexibility and reliability. In this sense, this paper presents a secondary layer control strategy that does not require the use of communications to operate in isolated microgrids with unbalanced loads. The strategy guarantees proper performance in terms of power-sharing between the distributed generators of the microgrid. Simulations on Matlab/Simulink are presented to validate the response of proposal.

Volume II: Energy Management Systems for Optimal Operation of Electrical Micro/Nanogrids

Electrical microgrids (MG) have emerged as one of the most promising solutions for the energy transition of electrical power systems [...]

Renewable energy-based electrical microgrid of cold ironing energy supply for berthed ships

The importance of ports, which are the gateways between maritime transport and other modes of transport, is growing every day. In addition, the amount of cargo that ports can handle is increasing rapidly every year. At the same time, the need for energy is increasing. Ships hoteling at ports account for a large portion of the power demand at ports. Today, ships hoteling at ports meet their energy needs with their own auxiliary engines running on fossil fuels. In order to achieve decarbonization and zero emissions targets, it is essential to minimize the use of fossil fuels in ports and to increase the use of renewable energy. In this context, meeting the ship's power needs in port through a renewable energy-based microgrid will help reduce emissions. In this study, after determining the energy needs, the scenarios developed with the HOMER program were used to design electrically and economically suitable microgrid systems and to meet the electricity needs of the ships in port using renewable energy.

Optimizing Distribution System Resilience in Extreme Weather Using Prosumer-Centric Microgrids with Integrated Distributed Energy Resources and Battery Electric Vehicles

Electric power networks face vulnerabilities from various hazards, including extreme weather and natural disasters, resulting in prolonged outages and service disruptions. This paper proposes prosumer-centric networked electrical microgrids as a solution. EMGs integrate DERs, like SPV panels, WTs, BESSs, and BEVs, to form autonomous microgrids capable of operating independently during grid disruptions. The SMA was used to identify the appropriate allocation of DERs and BEVs to improve the resilience of the system. Prosumers, acting as both producers and consumers, play a crucial role by generating and sharing electricity within the microgrid. BEVs act as mobile energy storage units during emergencies. Load management and demand response strategies prioritize the energy needs for essential facilities, ensuring uninterrupted operation during adverse weather. Robust communication and control systems improve the emergency coordination and response. The resilience analysis focused on two case studies: moderate and severe damage, both under varying weather conditions. Simulations and experiments assessed the microgrid performance with different levels of DERs and demand. By testing on the IEEE 69-bus RDS, evaluated the EMGs’ strengths and limitations, demonstrating their potential to enhance distribution grid resilience against natural disasters.

Artificial Intelligence Applied for Smart Electric Microgrids: A Literature Review

Energy production schemes are subject to a constant scientific and technological revolution due to fluctuating social, economic, and political changes. The integration of new ways of power generation, electrical and electronic elements that consume the power generated and require specific energy quality policies, electric vehicles, energy efficiency, energy management, architecture reliability, bidirectional communication, and resilience are some challenges control strategies face. Technology and science have supported and faced this challenge in energy production schemes, especially in intelligent electrical microgrids. This article presents a comprehensive review of the state of the art of artificial intelligence techniques that are applied to face the various challenges of operation, control, and coordination in a Smart Electric Microgrid (SEM). In particular, how artificial intelligence develops and faces challenges in central areas in an SEM architecture.

[formula omitted] gain-scheduled dynamic output feedback control with transient performance applied to electrical microgrid

This paper introduces an approach for designing gain-scheduled dynamic output feedback controllers for continuous-time Linear Parameter-Varying (LPV) systems. The aim is to improve transient response by incorporating both D-stability and H∞ criteria into the synthesis conditions. We achieve this by employing changes of variables and congruence transformations, which lead to new synthesis conditions expressed as linear matrix inequalities. Additional fine-tuned scalar parameters can be used to enhance further the controller performance in terms of less conservative H∞ guaranteed costs. To assess the effectiveness of our proposed synthesis procedure, we conduct computational experiments in the context of a microinverter-based distributed power generation system. These experiments take into account real-world operational characteristics, providing a comprehensive evaluation of our approach. The results demonstrate that the designed controller effectively ensures the desired closed-loop system behavior, even when dealing with state noise and discretization errors originating from digital implementation.

Optimal demand management in the electrical microgrid considering energy market prices uncertainty

The optimal management of electrical energy systems is a major concern for energy providers facing uncertainties. In this work, energy demand management and dispatch in the electricity microgrid are performed using an interval optimization strategy under conditions of electricity price uncertainty. Demand dispatch is implemented through the Demand Response Program (DRP) incentive modeling. As a solution, incentive modeling based on the price of the offer to reduce electricity demand during peak periods is proposed. In order to minimize the effects of electricity price uncertainty, the interval optimization approach is used. The main goal of the proposed approach is to minimize the operational cost, and the epsilon constraint-based fuzzy method was used to solve and handle the problem. Finally, the power microgrid was used in different case studies for the results of the numeric simulations and for the positive effects of the proposed modeling under conditions of uncertainty.

Phase Shift Modulation Strategy for Single Stage AC to DC Dual Active Bridge Converter

Energy exchange between AC grid and DC supply that is a part of a hybrid electric micro-grid takes place using various power converter designs. The single-phase, single-stage, AC-DC power dual active bridge converter is one option. The phase-shift modulation is used to regulate energy flow in both directions. The topology of one stage AC-DC dual active bridge converter based in bidirectional switching modules has been introduced. This paper next introduces the analysis of the AC side current considering basic modulation functions and suggests an optimum phase-shifted modulation strategy. The proposed modulation function provides minimum harmonics distortion. A simulation study is presented to compare the proposed strategy to the basic sinusoidal and triangular modulation techniques. The results show that the modified modulation reduces the average THD by about 55% and 39% compared to the standard sinusoidal and triangular modulation strategies respectively and ensures linear relationship between the transferred power and magnitude control coefficient.

DG Compensation Using Optimal Distribution in Microgrids with High DG Penetration

This study developed a model to compensate for both active and passive power in an electric microgrid with an optimal power flow to allow a detailed analysis of its power flow, which encourages the integration of renewable energy sources (RESs). Mixture integer linear programming (MILP) is proposed, with distributed generation (DG) integrated into the existing bars. It is developed in the GAMS tool to minimize operating costs in the microgrid as an objective function, to guarantee reliability, and quality in the microgrid. The algorithm calculates an optimal solution that meets all requirements for reliability and quality in the microgrid. Finally, a comparison of data between the two systems without and with DG was performed, showing an improvement in voltage profiles, reduced active power losses in the lines, a better power factor (PF), and improved angular behavior; that also adheres to the restrictions set forth at a minimum operating cost. A nine-bar model has been used to model the problem by IEEE standards.

Modeling smart electrical microgrid with demand response and storage systems for optimal operation in critical conditions

This study examines the issue in standard and operational scenarios of microgrids that arise during critical conditions. Initially, the ideal energy storage size and discharge depth are identified for optimal microgrid planning under operating conditions. Subsequently, by utilizing the energy storage system and load response, the microgrid’s vulnerability is reduced and the cost of load shedding is minimized when in critical conditions, leading to the microgrid being disconnected from the main network and operating in island mode. This model aims to analyze how the system performance is influenced by the presence of storage systems and load response programs in a numerical scenario, particularly during severe weather events. In addition, the study examines the role of advanced control algorithms and communication systems in optimizing the operation of the microgrid. By implementing smart grid technologies, the microgrid can better manage its energy resources, anticipate fluctuations in demand, and respond quickly to changing conditions. This proactive approach helps to ensure the stability and reliability of the microgrid, even in the face of unforeseen challenges. Overall, this research contributes valuable insights into the challenges and opportunities facing microgrids in both normal and emergency situations. By identifying the most effective energy storage solutions, load response strategies, renewable energy integration methods, and advanced control systems, the study aims to enhance the resilience, efficiency, and sustainability of microgrid systems in the future. The results of the proposed approach in critical operational mode represent the optimal condition of the electrical microgrid with minimum cost and load shedding considering storage systems and load response programs.

Optimal Generation Dispatch in Electrical Microgrids Based on Inertia Markets as a Solution to Frequency Stability

This paper addresses a crucial omission in the traditional approach to solving the classic economic dispatch problem within microgrids featuring renewable energy sources—the often-neglected frequency disturbances arising from reductions in system inertia. To remedy this, we present an innovative economic dispatch model empowered by nonlinear optimization (NLP), incorporating stringent minimum inertia constraints essential for ensuring system stability over a 24-h horizon. Our approach involves a comprehensive exploration of the intricate relationship between system inertia and frequency stability, culminating in the seamless integration of these inertia constraints into the economic dispatch model. To validate the practicality of our model, we present two distinct scenarios: a base case representing conventional dispatch methodologies and an alternative case that considers the imposition of inertia restrictions. These scenarios are rigorously tested and implemented using the CICGRE TF C6.04 test system. Employing the powerful GAMS platform alongside the NPL model, we successfully solved the dispatch problem. Our results underscore the significance of maintaining system inertia within the 1.54-s threshold proposed by our model, showcasing a tangible reduction in generation costs as a direct outcome of this enhanced approach to economic dispatch. This research advances the understanding of microgrid management and offers a practical solution to enhance system stability and economic efficiency in renewable-energy-powered microgrids.

Revolutionizing Green Transport: An Extensive Review of Hybrid Electric Vehicle Charging Stations and Electric Microgrid Integration

Electric vehicles (EVs), recognized as a strategic approach to reducing oil consumption and greenhouse gas emissions, rely on electricity instead of traditional fuels like petrol or diesel for battery charging, positioning them as a significant player in future energy landscapes. The anticipated decline in oil demand aligns with the increasing prevalence of EVs, making attention to charging infrastructure crucial. This paper extensively explores charging infrastructure considerations, emphasizing their significance in both urban and rural contexts, especially in regions with unstable or absent power supplies. Examining off-grid, grid-connected, and hybrid charging modes, the research delves into various EV designs, including those utilizing fuel cells or batteries.
 A thorough understanding of energy-source-based charging techniques and diverse power-level charging stations is presented, catering to readers' interests. With a focus on enhancing the longevity and efficiency of electric vehicles, researchers are investigating innovative charging methods, including microgrid concepts within charging stations. Recognizing electric vehicles as multi-energy systems, the paper underscores the importance of effective power management and control for optimal energy utilization.
 Additionally, the review scrutinizes the impact of electric vehicles on utility grid infrastructure & maintenance, evaluating various power management and control systems. This comprehensive review serves as a valuable resource for electric vehicle operators and research engineers, offering insights into the current state of the field and potential avenues for future exploration.

Economic and environmental benefits of decentralized multi-energy systems for energy communities

In the context of growing interest in decentralised multi energy systems, this work aims at quantifying the benefit of optimized energy concepts for energy communities at the neighbourhood scale compared to individually optimized solution. To tackle this question a multi-objective optimization framework was developed and applied to a case study of 6 buildings consisting of 85% of residential dwellings and 15% to retail shop and food stores. Grouped buildings have decreased costs and greenhouse gases emissions (GHGE) respectively by 18% and 12% in the cost optimum compared to individual buildings. In the environmental optimum, costs have decreased by 11%, while GHGE remains in the same range. This decrease is at both optimum driven by electricity prices favourable to large consumers since exchanges on the electrical microgrid for this neighbourhood is very small. Optimal decrease of GHGE is obtained with greater use of HPs and smaller natural gas consumption. This work illustrates the interest of multi-objective approaches to identify optimal energy solutions for groups of buildings.